Document Type
Article
Source Publication Title
Conference Proceedings of the American Society for Composites – 36th Technical Conference on Composite Materials, Sep 2021, College Station, TX
Abstract
The durability and residual load carrying capacity of composite materials and structures is of critical importance for increasing their application across the industry. Regularized eXtended Finite Element Method (Rx-FEM) framework for discrete modeling of damage evolution and interaction in laminated composite materials under fatigue loading has been extended to include residual Strength Tracking (ST) method in the Mesh Independent Crack (MIC) insertion constitutive modeling as well as in the initiation phase of the fatigue Cohesive Zone Model (CZM). The ST method was initially proposed for semi empirical analysis of IM7/8852 open-hole specimen test S-N data under spectrum loading and variable R -ratios (R = 0.1, 5, and -1). In the present work, the ST method is implemented as a component of high-fidelity progressive damage analysis framework and is combined with damage variable incremental update technique for the propagation phase within the CZM formulation. 3D validation examples investigating the solution stability under constant amplitude fatigue with respect to the cycle increment size are presented and demonstrate the solution stability within a feasible range of cycles per step. [Conference: Proceedings of the American Society for Composites – 36th Technical Conference on Composite Materials Sep 2021 Location: College Station, TX DOI: 10.12783/asc36/35893]
Disciplines
Engineering | Materials Science and Engineering
Publication Date
9-1-2021
Language
English
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Lu, Wei-Tsen; Gao, Zhenjia; Adluru, Hari K.; Hoos, Kevin H.; Seneviratne, Waruna; and Mollenhauer, David, "Fatigue Damage Modeling in Laminated Composites by using Rx-FEM and Strength Tracking Method" (2021). Institute of Predictive Performance Methodologies (IPPM-UTARI). 34.
https://mavmatrix.uta.edu/utari_ippm/34
Comments
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